Create app.py

app.py

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+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")
+model = AutoModelForSequenceClassification.from_pretrained("mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")
+
+# Set the model to evaluation mode
+model.eval()
+
+def predict_sentiment(text):
+    # Tokenize the input text and prepare it to be used as model input
+    inputs = tokenizer.encode_plus(
+        text,
+        add_special_tokens=True,
+        return_tensors="pt",
+        max_length=512,  # You can adjust the max length based on your requirements
+        truncation=True
+    )
+
+    # Perform the prediction
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predicted_class = torch.argmax(outputs.logits, dim=1).item()
+    
+    # Determine the sentiment based on the predicted class
+    if predicted_class == 0:
+        return "Negative sentiment"
+    elif predicted_class == 1:
+        return "Neutral sentiment"
+    elif predicted_class == 2:
+        return "Positive sentiment"
+
+# Streamlit interface
+st.title("Financial News Sentiment Analysis")
+
+user_input = st.text_area("Enter the financial news text:")
+if st.button("Analyze"):
+    if user_input:
+        # Get the sentiment prediction
+        sentiment = predict_sentiment(user_input)
+        st.write("Predicted sentiment:", sentiment)
+    else:
+        st.write("Please enter some text.")